The growth of the Internet of Things (IoT) continues to be rapid, making it an essential part of information technology. As a result, IoT devices must be able to handle data collection, machine-to-machine (M2M) communication, and preprocessing of data, while also considering cost, processing power, and energy consumption. This paper introduces a system for device indoor localization that uses variations in the strength of the wireless signal. The proposed system addresses logistics use cases in which it is imperative to achieve reliable end-to-end delivery, such as pharmaceutic delivery, delivery of confidential documents and court exhibits, and even food, since the same is introduced into human organism and presents a potential risk of terrorist or other attack. This work proposes a concept based on low-power and low-cost LoRaWAN based system that utilizes a Machine Learning technique based on Neural Networks to achieve high accuracy in device indoor localization by measuring the signal strength of a beacon device. Furthermore, using signal strength measurements, that is, RSSI and SNR captured by LoRaWAN gateways, it is possible to estimate the location of the device point with an accuracy of up to 98.8%.

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions.

LoRaWANs are increasingly recognized for their vulnerability to various jamming attacks, which can significantly disrupt communication between end nodes and gateways. This paper explores the feasibility of executing reactive jammers upon detecting packet transmission using commercially available equipment based on software-defined radios (SDRs). The proposed approach demonstrates how attackers can exploit packet detection to initiate targeted interference, effectively compromising message integrity. Two distinct experimental setups, one using separate SDRs for reception and transmission, and another leveraging a single SDR for both functions, were used to evaluate attack efficiency, reaction times, and packet loss ratios. Our experiments demonstrate that both scenarios effectively jam LoRaWAN packets across a range of spreading factors and payload sizes. This finding underscores a pressing need for enhanced security measures to maintain reliability and counter sophisticated attacks.

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions.

LoRaWAN networks are increasingly recognized for their vulnerability to various jamming attacks, which can significantly disrupt communication between end nodes and gateways. This paper explores the feasibility of executing reactive jammers upon detecting packet transmission using commercially available equipment based on Software-Defined Radios (SDRs). The proposed approach demonstrates how attackers can exploit packet detection to initiate targeted interference, effectively compromising message integrity. Two distinct experimental setups, one using separate SDRs for reception and transmission, and another leveraging a single SDR for both functions, were used to evaluate attack efficiency, reaction times, and packet loss ratios. Our experiments demonstrate that both scenarios effectively jam LoRaWAN packets across a range of spreading factors and payload sizes. This finding underscores a pressing need for enhanced security measures to maintain reliability and counter sophisticated attacks.

Currently, monitoring systems in various fields rely on commercial technologies that can be costly and, more critically, lack integration with remote management solutions. These systems often present accessibility challenges for researchers and professionals who require data collection for in-depth analysis. In addition, the deployment of sensing devices within more complex networks can be difficult, hindering the scalability and effectiveness of these technologies. This work presents a flexible smart multitechnological datalogger based on an Internet of Things (IoT) structure that utilizes numerous Low Power Wide Area Network solutions, allowing remote analysis of the phenomenon and reducing the installation and management complexity. The designed system was developed at a lower cost than state-of-the-art dataloggers and tested in the hydric monitoring scenario. The results indicate that the system can sustain remote monitoring operations for a significant duration without frequent battery replacements, making it suitable for applications that require extended autonomous deployments.